Overview

Dataset statistics

Number of variables26
Number of observations37687
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 MiB
Average record size in memory272.4 B

Variable types

Numeric20
Categorical6

Warnings

cand_id has a high cardinality: 37687 distinct values High cardinality
sumClassADonation is highly correlated with maxClassADonationHigh correlation
sumClassBDonation is highly correlated with maxClassBDonationHigh correlation
sumClassDDonation is highly correlated with maxClassDDonationHigh correlation
maxClassADonation is highly correlated with sumClassADonationHigh correlation
maxClassBDonation is highly correlated with sumClassBDonationHigh correlation
maxClassDDonation is highly correlated with sumClassDDonationHigh correlation
NetWorth is highly skewed (γ1 = 96.39810349) Skewed
sumClassADonation is highly skewed (γ1 = 114.871994) Skewed
sumClassBDonation is highly skewed (γ1 = 133.7955175) Skewed
sumClassCDonation is highly skewed (γ1 = 26.65834009) Skewed
sumClassDDonation is highly skewed (γ1 = 134.5664897) Skewed
maxClassADonation is highly skewed (γ1 = 110.0947824) Skewed
maxClassBDonation is highly skewed (γ1 = 132.1183179) Skewed
maxClassCDonation is highly skewed (γ1 = 31.02000162) Skewed
maxClassDDonation is highly skewed (γ1 = 115.9107361) Skewed
sumCauseEDonations is highly skewed (γ1 = 21.28377585) Skewed
rolling_avg is highly skewed (γ1 = 65.93294395) Skewed
cand_id is uniformly distributed Uniform
df_index has unique values Unique
cand_id has unique values Unique
totalHouseholdDebt has 6437 (17.1%) zeros Zeros
primaryPropertyLoanToValue has 7240 (19.2%) zeros Zeros
propertyCount has 918 (2.4%) zeros Zeros
sumCauseADonations has 33076 (87.8%) zeros Zeros
sumCauseBDonations has 36434 (96.7%) zeros Zeros
sumCauseCDonations has 25658 (68.1%) zeros Zeros
sumCauseDDonations has 29795 (79.1%) zeros Zeros
sumCauseEDonations has 37580 (99.7%) zeros Zeros

Reproduction

Analysis started2021-03-23 16:26:54.309353
Analysis finished2021-03-23 16:27:42.271570
Duration47.96 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct37687
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30978.7358
Minimum0
Maximum74602
Zeros1
Zeros (%)< 0.1%
Memory size294.6 KiB
2021-03-23T11:27:42.368058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1977.3
Q19978.5
median19936
Q361033
95-th percentile72717.7
Maximum74602
Range74602
Interquartile range (IQR)51054.5

Descriptive statistics

Standard deviation25695.3097
Coefficient of variation (CV)0.8294499126
Kurtosis-1.286806854
Mean30978.7358
Median Absolute Deviation (MAD)14304
Skewness0.5815111918
Sum1167495616
Variance660248940.5
MonotocityStrictly increasing
2021-03-23T11:27:42.480264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
8211
 
< 0.1%
727461
 
< 0.1%
192281
 
< 0.1%
171811
 
< 0.1%
233261
 
< 0.1%
212791
 
< 0.1%
417611
 
< 0.1%
458591
 
< 0.1%
602001
 
< 0.1%
Other values (37677)37677
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
746021
< 0.1%
746011
< 0.1%
746001
< 0.1%
745991
< 0.1%
745981
< 0.1%
745971
< 0.1%
745961
< 0.1%
745951
< 0.1%
745941
< 0.1%
745931
< 0.1%

cand_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct37687
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
candidate_818
 
1
candidate_7569
 
1
candidate_99580
 
1
candidate_6108
 
1
candidate_100024
 
1
Other values (37682)
37682 

Length

Max length16
Median length16
Mean length15.37092366
Min length11

Characters and Unicode

Total characters579284
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37687 ?
Unique (%)100.0%

Sample

1st rowcandidate_0
2nd rowcandidate_1
3rd rowcandidate_10
4th rowcandidate_100
5th rowcandidate_1000
ValueCountFrequency (%)
candidate_8181
 
< 0.1%
candidate_75691
 
< 0.1%
candidate_995801
 
< 0.1%
candidate_61081
 
< 0.1%
candidate_1000241
 
< 0.1%
candidate_1015511
 
< 0.1%
candidate_1066891
 
< 0.1%
candidate_70291
 
< 0.1%
candidate_934491
 
< 0.1%
candidate_1255511
 
< 0.1%
Other values (37677)37677
> 99.9%
2021-03-23T11:27:42.786174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
candidate_8181
 
< 0.1%
candidate_75691
 
< 0.1%
candidate_995801
 
< 0.1%
candidate_61081
 
< 0.1%
candidate_1000241
 
< 0.1%
candidate_1015511
 
< 0.1%
candidate_1066891
 
< 0.1%
candidate_70291
 
< 0.1%
candidate_934491
 
< 0.1%
candidate_1255511
 
< 0.1%
Other values (37677)37677
> 99.9%

Most occurring characters

ValueCountFrequency (%)
a75374
13.0%
d75374
13.0%
144963
 
7.8%
c37687
 
6.5%
n37687
 
6.5%
i37687
 
6.5%
t37687
 
6.5%
e37687
 
6.5%
_37687
 
6.5%
922847
 
3.9%
Other values (8)134604
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter339183
58.6%
Decimal Number202414
34.9%
Connector Punctuation37687
 
6.5%

Most frequent character per category

ValueCountFrequency (%)
144963
22.2%
922847
11.3%
222470
11.1%
021566
10.7%
815536
 
7.7%
315117
 
7.5%
715073
 
7.4%
414975
 
7.4%
514969
 
7.4%
614898
 
7.4%
ValueCountFrequency (%)
a75374
22.2%
d75374
22.2%
c37687
11.1%
n37687
11.1%
i37687
11.1%
t37687
11.1%
e37687
11.1%
ValueCountFrequency (%)
_37687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin339183
58.6%
Common240101
41.4%

Most frequent character per script

ValueCountFrequency (%)
144963
18.7%
_37687
15.7%
922847
9.5%
222470
9.4%
021566
9.0%
815536
 
6.5%
315117
 
6.3%
715073
 
6.3%
414975
 
6.2%
514969
 
6.2%
ValueCountFrequency (%)
a75374
22.2%
d75374
22.2%
c37687
11.1%
n37687
11.1%
i37687
11.1%
t37687
11.1%
e37687
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII579284
100.0%

Most frequent character per block

ValueCountFrequency (%)
a75374
13.0%
d75374
13.0%
144963
 
7.8%
c37687
 
6.5%
n37687
 
6.5%
i37687
 
6.5%
t37687
 
6.5%
e37687
 
6.5%
_37687
 
6.5%
922847
 
3.9%
Other values (8)134604
23.2%

totalHouseholdDebt
Real number (ℝ≥0)

ZEROS

Distinct18866
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.400261735
Minimum0
Maximum7.226079549
Zeros6437
Zeros (%)17.1%
Memory size294.6 KiB
2021-03-23T11:27:42.895047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.50571275
median5.266727817
Q35.609055742
95-th percentile6.034610167
Maximum7.226079549
Range7.226079549
Interquartile range (IQR)1.103342992

Descriptive statistics

Standard deviation2.060761799
Coefficient of variation (CV)0.4683270957
Kurtosis0.6478226791
Mean4.400261735
Median Absolute Deviation (MAD)0.4335998374
Skewness-1.525320715
Sum165832.664
Variance4.246739191
MonotocityNot monotonic
2021-03-23T11:27:43.003016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06437
 
17.1%
5.70791066674
 
0.2%
5.47712125557
 
0.2%
551
 
0.1%
5.69897000450
 
0.1%
5.39794000949
 
0.1%
4.69897000441
 
0.1%
5.60205999138
 
0.1%
5.30102999636
 
0.1%
5.70757017634
 
0.1%
Other values (18856)30820
81.8%
ValueCountFrequency (%)
06437
17.1%
1.9242792861
 
< 0.1%
2.1760912592
 
< 0.1%
2.287801731
 
< 0.1%
2.6253124512
 
< 0.1%
2.6263403671
 
< 0.1%
2.6937269491
 
< 0.1%
2.7024305362
 
< 0.1%
2.7176705032
 
< 0.1%
2.7874604752
 
< 0.1%
ValueCountFrequency (%)
7.2260795491
< 0.1%
7.1562782232
< 0.1%
7.0580775381
< 0.1%
7.0546637962
< 0.1%
7.0167235431
< 0.1%
6.9499088491
< 0.1%
6.9258412271
< 0.1%
6.9112177132
< 0.1%
6.9075405012
< 0.1%
6.8983427571
< 0.1%

primaryPropertyLoanToValue
Real number (ℝ≥0)

ZEROS

Distinct21367
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2187765836
Minimum0
Maximum2.266595909
Zeros7240
Zeros (%)19.2%
Memory size294.6 KiB
2021-03-23T11:27:43.125361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02120021324
median0.1480936533
Q30.3490396387
95-th percentile0.6705787503
Maximum2.266595909
Range2.266595909
Interquartile range (IQR)0.3278394254

Descriptive statistics

Standard deviation0.2304396471
Coefficient of variation (CV)1.053310383
Kurtosis2.839515297
Mean0.2187765836
Median Absolute Deviation (MAD)0.147488224
Skewness1.3981127
Sum8245.033105
Variance0.05310243096
MonotocityNot monotonic
2021-03-23T11:27:43.235526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07240
 
19.2%
0.246735645
 
< 0.1%
0.024147173495
 
< 0.1%
0.51906118724
 
< 0.1%
0.14718734074
 
< 0.1%
0.094910138724
 
< 0.1%
0.22306512644
 
< 0.1%
0.072159541264
 
< 0.1%
0.088414367264
 
< 0.1%
0.080422037424
 
< 0.1%
Other values (21357)30409
80.7%
ValueCountFrequency (%)
07240
19.2%
4.445629946 × 1072
 
< 0.1%
4.81000481 × 1071
 
< 0.1%
6.176652254 × 1071
 
< 0.1%
8.582401785 × 1071
 
< 0.1%
8.930349525 × 1071
 
< 0.1%
9.081860256 × 1072
 
< 0.1%
9.221731933 × 1071
 
< 0.1%
9.247477288 × 1072
 
< 0.1%
1.03409729 × 1062
 
< 0.1%
ValueCountFrequency (%)
2.2665959091
< 0.1%
1.8993558881
< 0.1%
1.8779219931
< 0.1%
1.8770042191
< 0.1%
1.8448590981
< 0.1%
1.8386105621
< 0.1%
1.8194104791
< 0.1%
1.8126588921
< 0.1%
1.7928888332
< 0.1%
1.7706982952
< 0.1%

primaryPropertyValue
Real number (ℝ≥0)

Distinct21215
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1247840.017
Minimum1
Maximum27567207
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:43.355187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile361000
Q1649934
median937640
Q31473271.5
95-th percentile3002296.5
Maximum27567207
Range27567206
Interquartile range (IQR)823337.5

Descriptive statistics

Standard deviation1118399.591
Coefficient of variation (CV)0.8962684119
Kurtosis58.16906907
Mean1247840.017
Median Absolute Deviation (MAD)352917
Skewness5.247990501
Sum4.702734673 × 1010
Variance1.250817644 × 1012
MonotocityNot monotonic
2021-03-23T11:27:43.464769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1173
 
0.5%
66117822
 
0.1%
61300020
 
0.1%
62000017
 
< 0.1%
60800017
 
< 0.1%
62200017
 
< 0.1%
56900017
 
< 0.1%
71900017
 
< 0.1%
93400017
 
< 0.1%
69400016
 
< 0.1%
Other values (21205)37354
99.1%
ValueCountFrequency (%)
1173
0.5%
200001
 
< 0.1%
218401
 
< 0.1%
227602
 
< 0.1%
242412
 
< 0.1%
245592
 
< 0.1%
253392
 
< 0.1%
254262
 
< 0.1%
272111
 
< 0.1%
296281
 
< 0.1%
ValueCountFrequency (%)
275672071
< 0.1%
260401722
< 0.1%
222640001
< 0.1%
222135001
< 0.1%
190870572
< 0.1%
186041881
< 0.1%
162037972
< 0.1%
160333332
< 0.1%
158429241
< 0.1%
155645212
< 0.1%

propertyCount
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.39233688
Minimum0
Maximum26
Zeros918
Zeros (%)2.4%
Memory size294.6 KiB
2021-03-23T11:27:43.565491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum26
Range26
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9419395363
Coefficient of variation (CV)0.6765169764
Kurtosis37.81122818
Mean1.39233688
Median Absolute Deviation (MAD)0
Skewness3.855366455
Sum52473
Variance0.88725009
MonotocityNot monotonic
2021-03-23T11:27:43.649839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
127139
72.0%
26183
 
16.4%
31999
 
5.3%
0918
 
2.4%
4789
 
2.1%
5381
 
1.0%
6184
 
0.5%
750
 
0.1%
815
 
< 0.1%
914
 
< 0.1%
Other values (6)15
 
< 0.1%
ValueCountFrequency (%)
0918
 
2.4%
127139
72.0%
26183
 
16.4%
31999
 
5.3%
4789
 
2.1%
5381
 
1.0%
6184
 
0.5%
750
 
0.1%
815
 
< 0.1%
914
 
< 0.1%
ValueCountFrequency (%)
261
 
< 0.1%
212
 
< 0.1%
201
 
< 0.1%
124
 
< 0.1%
114
 
< 0.1%
103
 
< 0.1%
914
 
< 0.1%
815
 
< 0.1%
750
 
0.1%
6184
0.5%

isClassADonor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0.0
37658 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters113061
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.037658
99.9%
1.029
 
0.1%
2021-03-23T11:27:43.833094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T11:27:43.888823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.037658
99.9%
1.029
 
0.1%

Most occurring characters

ValueCountFrequency (%)
075345
66.6%
.37687
33.3%
129
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75374
66.7%
Other Punctuation37687
33.3%

Most frequent character per category

ValueCountFrequency (%)
075345
> 99.9%
129
 
< 0.1%
ValueCountFrequency (%)
.37687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common113061
100.0%

Most frequent character per script

ValueCountFrequency (%)
075345
66.6%
.37687
33.3%
129
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII113061
100.0%

Most frequent character per block

ValueCountFrequency (%)
075345
66.6%
.37687
33.3%
129
 
< 0.1%

isClassBDonor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
1.0
19305 
0.0
18382 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters113061
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.019305
51.2%
0.018382
48.8%
2021-03-23T11:27:44.028899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T11:27:44.090990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.019305
51.2%
0.018382
48.8%

Most occurring characters

ValueCountFrequency (%)
056069
49.6%
.37687
33.3%
119305
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75374
66.7%
Other Punctuation37687
33.3%

Most frequent character per category

ValueCountFrequency (%)
056069
74.4%
119305
 
25.6%
ValueCountFrequency (%)
.37687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common113061
100.0%

Most frequent character per script

ValueCountFrequency (%)
056069
49.6%
.37687
33.3%
119305
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII113061
100.0%

Most frequent character per block

ValueCountFrequency (%)
056069
49.6%
.37687
33.3%
119305
 
17.1%

isClassCDonor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0.0
27731 
1.0
9956 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters113061
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.027731
73.6%
1.09956
 
26.4%
2021-03-23T11:27:44.243835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T11:27:44.300501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.027731
73.6%
1.09956
 
26.4%

Most occurring characters

ValueCountFrequency (%)
065418
57.9%
.37687
33.3%
19956
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75374
66.7%
Other Punctuation37687
33.3%

Most frequent character per category

ValueCountFrequency (%)
065418
86.8%
19956
 
13.2%
ValueCountFrequency (%)
.37687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common113061
100.0%

Most frequent character per script

ValueCountFrequency (%)
065418
57.9%
.37687
33.3%
19956
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII113061
100.0%

Most frequent character per block

ValueCountFrequency (%)
065418
57.9%
.37687
33.3%
19956
 
8.8%

isClassDDonor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0.0
30694 
1.0
6993 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters113061
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.030694
81.4%
1.06993
 
18.6%
2021-03-23T11:27:44.437958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T11:27:44.498318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.030694
81.4%
1.06993
 
18.6%

Most occurring characters

ValueCountFrequency (%)
068381
60.5%
.37687
33.3%
16993
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75374
66.7%
Other Punctuation37687
33.3%

Most frequent character per category

ValueCountFrequency (%)
068381
90.7%
16993
 
9.3%
ValueCountFrequency (%)
.37687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common113061
100.0%

Most frequent character per script

ValueCountFrequency (%)
068381
60.5%
.37687
33.3%
16993
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII113061
100.0%

Most frequent character per block

ValueCountFrequency (%)
068381
60.5%
.37687
33.3%
16993
 
6.2%

NetWorth
Real number (ℝ≥0)

SKEWED

Distinct26265
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4427231.996
Minimum1
Maximum3246185623
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:44.576059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile464738.6
Q11088610
median2104011
Q34552962
95-th percentile13687895
Maximum3246185623
Range3246185622
Interquartile range (IQR)3464352

Descriptive statistics

Standard deviation27325480.11
Coefficient of variation (CV)6.172136481
Kurtosis10841.89412
Mean4427231.996
Median Absolute Deviation (MAD)1262772
Skewness96.39810349
Sum1.668490922 × 1011
Variance7.466818632 × 1014
MonotocityNot monotonic
2021-03-23T11:27:44.686562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110
 
< 0.1%
407087045
 
< 0.1%
10801055
 
< 0.1%
8318255
 
< 0.1%
11315335
 
< 0.1%
14557435
 
< 0.1%
10254255
 
< 0.1%
3338004
 
< 0.1%
10397674
 
< 0.1%
27157384
 
< 0.1%
Other values (26255)37635
99.9%
ValueCountFrequency (%)
110
< 0.1%
50071
 
< 0.1%
148251
 
< 0.1%
247791
 
< 0.1%
283922
 
< 0.1%
300361
 
< 0.1%
366921
 
< 0.1%
396501
 
< 0.1%
409121
 
< 0.1%
414081
 
< 0.1%
ValueCountFrequency (%)
32461856232
< 0.1%
13299005811
< 0.1%
11689026042
< 0.1%
5204658291
< 0.1%
4499816172
< 0.1%
2764617492
< 0.1%
2663944072
< 0.1%
2610524082
< 0.1%
1923005341
< 0.1%
1751554732
< 0.1%

sumClassADonation
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1300.606071
Minimum1
Maximum14172001
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:44.789047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum14172001
Range14172000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation111146.9812
Coefficient of variation (CV)85.45783666
Kurtosis14206.91804
Mean1300.606071
Median Absolute Deviation (MAD)0
Skewness114.871994
Sum49015941
Variance1.235365142 × 1010
MonotocityNot monotonic
2021-03-23T11:27:44.873594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
137658
99.9%
8461052
 
< 0.1%
3050012
 
< 0.1%
52552
 
< 0.1%
30308282
 
< 0.1%
2890582
 
< 0.1%
50012
 
< 0.1%
1550012
 
< 0.1%
4374942
 
< 0.1%
46000012
 
< 0.1%
Other values (7)11
 
< 0.1%
ValueCountFrequency (%)
137658
99.9%
30012
 
< 0.1%
50012
 
< 0.1%
52552
 
< 0.1%
60011
 
< 0.1%
100011
 
< 0.1%
250012
 
< 0.1%
1073932
 
< 0.1%
1550012
 
< 0.1%
2890582
 
< 0.1%
ValueCountFrequency (%)
141720012
< 0.1%
46000012
< 0.1%
30308282
< 0.1%
10000011
< 0.1%
8461052
< 0.1%
4374942
< 0.1%
3050012
< 0.1%
2890582
< 0.1%
1550012
< 0.1%
1073932
< 0.1%

sumClassBDonation
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2821
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3365.209342
Minimum1
Maximum31044572.33
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:44.980759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q3201
95-th percentile2366
Maximum31044572.33
Range31044571.33
Interquartile range (IQR)200

Descriptive statistics

Standard deviation228135.4503
Coefficient of variation (CV)67.79235022
Kurtosis18190.65033
Mean3365.209342
Median Absolute Deviation (MAD)5
Skewness133.7955175
Sum126824644.5
Variance5.204578368 × 1010
MonotocityNot monotonic
2021-03-23T11:27:45.093557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118541
49.2%
26904
 
2.4%
51865
 
2.3%
101760
 
2.0%
21594
 
1.6%
16540
 
1.4%
11426
 
1.1%
201312
 
0.8%
31309
 
0.8%
76299
 
0.8%
Other values (2811)14137
37.5%
ValueCountFrequency (%)
118541
49.2%
219
 
0.1%
331
 
0.1%
421
 
0.1%
510
 
< 0.1%
5.392
 
< 0.1%
6225
 
0.6%
711
 
< 0.1%
89
 
< 0.1%
8.54
 
< 0.1%
ValueCountFrequency (%)
31044572.332
< 0.1%
2094913.71
< 0.1%
18400062
< 0.1%
17600011
< 0.1%
1677379.241
< 0.1%
15881101
< 0.1%
12706012
< 0.1%
9921111
< 0.1%
9468711
< 0.1%
700364.241
< 0.1%

sumClassCDonation
Real number (ℝ≥0)

SKEWED

Distinct3320
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1146.693525
Minimum1
Maximum535326
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:45.207464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q321
95-th percentile3400.94
Maximum535326
Range535325
Interquartile range (IQR)20

Descriptive statistics

Standard deviation10075.36505
Coefficient of variation (CV)8.786449767
Kurtosis995.9970471
Mean1146.693525
Median Absolute Deviation (MAD)0
Skewness26.65834009
Sum43215438.87
Variance101512981
MonotocityNot monotonic
2021-03-23T11:27:45.318675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127741
73.6%
501230
 
0.6%
101221
 
0.6%
51221
 
0.6%
251198
 
0.5%
26190
 
0.5%
1001148
 
0.4%
201110
 
0.3%
11100
 
0.3%
30189
 
0.2%
Other values (3310)8439
 
22.4%
ValueCountFrequency (%)
127741
73.6%
235
 
0.1%
2.12
 
< 0.1%
2.52
 
< 0.1%
34
 
< 0.1%
3.22
 
< 0.1%
3.42
 
< 0.1%
3.74
 
< 0.1%
419
 
0.1%
4.64
 
< 0.1%
ValueCountFrequency (%)
5353262
< 0.1%
457037.722
< 0.1%
433716.521
< 0.1%
337266.931
< 0.1%
301350.141
< 0.1%
2982211
< 0.1%
287588.52
< 0.1%
2786312
< 0.1%
271251.41
< 0.1%
2675372
< 0.1%

sumClassDDonation
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1189
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1170.462614
Minimum1
Maximum12094601
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:45.425864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile964.5
Maximum12094601
Range12094600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation88712.92202
Coefficient of variation (CV)75.79304194
Kurtosis18328.65113
Mean1170.462614
Median Absolute Deviation (MAD)0
Skewness134.5664897
Sum44111224.55
Variance7869982534
MonotocityNot monotonic
2021-03-23T11:27:45.542586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130694
81.4%
101928
 
2.5%
201363
 
1.0%
501247
 
0.7%
251225
 
0.6%
51185
 
0.5%
301155
 
0.4%
1001153
 
0.4%
26110
 
0.3%
151107
 
0.3%
Other values (1179)4520
 
12.0%
ValueCountFrequency (%)
130694
81.4%
1.61
 
< 0.1%
23
 
< 0.1%
2.782
 
< 0.1%
311
 
< 0.1%
3.251
 
< 0.1%
3.41
 
< 0.1%
3.56
 
< 0.1%
46
 
< 0.1%
4.331
 
< 0.1%
ValueCountFrequency (%)
120946012
< 0.1%
12675511
< 0.1%
6951012
< 0.1%
405833.032
< 0.1%
346538.152
< 0.1%
3147011
< 0.1%
258985.321
< 0.1%
231412.232
< 0.1%
2017511
< 0.1%
1920011
< 0.1%

maxClassADonation
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean578.7220262
Minimum1
Maximum5685001
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:45.640631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum5685001
Range5685000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation45392.93227
Coefficient of variation (CV)78.43650358
Kurtosis13278.7056
Mean578.7220262
Median Absolute Deviation (MAD)0
Skewness110.0947824
Sum21810297
Variance2060518300
MonotocityNot monotonic
2021-03-23T11:27:45.716523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
137658
99.9%
50013
 
< 0.1%
400012
 
< 0.1%
2443322
 
< 0.1%
20000012
 
< 0.1%
250012
 
< 0.1%
1000012
 
< 0.1%
8461052
 
< 0.1%
723932
 
< 0.1%
2890582
 
< 0.1%
Other values (6)10
 
< 0.1%
ValueCountFrequency (%)
137658
99.9%
25012
 
< 0.1%
50013
 
< 0.1%
52552
 
< 0.1%
60011
 
< 0.1%
250012
 
< 0.1%
400012
 
< 0.1%
723932
 
< 0.1%
1000012
 
< 0.1%
2443322
 
< 0.1%
ValueCountFrequency (%)
56850012
< 0.1%
20000012
< 0.1%
10661682
< 0.1%
10000011
< 0.1%
8461052
< 0.1%
2890582
< 0.1%
2443322
< 0.1%
1000012
< 0.1%
723932
< 0.1%
400012
< 0.1%

maxClassBDonation
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct602
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1560.802022
Minimum1
Maximum15832747
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:45.816021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q351
95-th percentile501
Maximum15832747
Range15832746
Interquartile range (IQR)50

Descriptive statistics

Standard deviation116899.6026
Coefficient of variation (CV)74.89713683
Kurtosis17854.53066
Mean1560.802022
Median Absolute Deviation (MAD)5
Skewness132.1183179
Sum58821945.81
Variance1.366551709 × 1010
MonotocityNot monotonic
2021-03-23T11:27:45.929014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118541
49.2%
1012892
 
7.7%
512408
 
6.4%
262278
 
6.0%
211177
 
3.1%
16990
 
2.6%
11819
 
2.2%
36660
 
1.8%
201633
 
1.7%
501615
 
1.6%
Other values (592)6674
 
17.7%
ValueCountFrequency (%)
118541
49.2%
221
 
0.1%
349
 
0.1%
428
 
0.1%
56
 
< 0.1%
5.392
 
< 0.1%
6386
 
1.0%
6.761
 
< 0.1%
73
 
< 0.1%
819
 
0.1%
ValueCountFrequency (%)
158327472
< 0.1%
18300062
< 0.1%
15010011
< 0.1%
10000011
< 0.1%
5804422
< 0.1%
558730.52
< 0.1%
5000012
< 0.1%
4030701
< 0.1%
4012612
< 0.1%
4000011
< 0.1%

maxClassCDonation
Real number (ℝ≥0)

SKEWED

Distinct412
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.3552936
Minimum1
Maximum100001
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:46.050546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q311
95-th percentile751
Maximum100001
Range100000
Interquartile range (IQR)10

Descriptive statistics

Standard deviation1738.999881
Coefficient of variation (CV)8.767095897
Kurtosis1312.792037
Mean198.3552936
Median Absolute Deviation (MAD)0
Skewness31.02000162
Sum7475415.95
Variance3024120.585
MonotocityNot monotonic
2021-03-23T11:27:46.640812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127739
73.6%
1011134
 
3.0%
251908
 
2.4%
501905
 
2.4%
51880
 
2.3%
26835
 
2.2%
1001775
 
2.1%
201276
 
0.7%
301224
 
0.6%
11214
 
0.6%
Other values (402)3797
 
10.1%
ValueCountFrequency (%)
127739
73.6%
247
 
0.1%
2.042
 
< 0.1%
2.52
 
< 0.1%
34
 
< 0.1%
3.714
 
< 0.1%
432
 
0.1%
4.51
 
< 0.1%
52
 
< 0.1%
5.21
 
< 0.1%
ValueCountFrequency (%)
1000013
 
< 0.1%
668012
 
< 0.1%
625012
 
< 0.1%
540012
 
< 0.1%
500018
< 0.1%
355011
 
< 0.1%
334013
 
< 0.1%
320012
 
< 0.1%
308012
 
< 0.1%
304011
 
< 0.1%

maxClassDDonation
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct341
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.1943116
Minimum1
Maximum1200001
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:46.748506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile401
Maximum1200001
Range1200000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9388.645049
Coefficient of variation (CV)44.24550771
Kurtosis14393.84594
Mean212.1943116
Median Absolute Deviation (MAD)0
Skewness115.9107361
Sum7996967.02
Variance88146655.86
MonotocityNot monotonic
2021-03-23T11:27:46.867216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130694
81.4%
1011696
 
4.5%
501681
 
1.8%
251572
 
1.5%
1001539
 
1.4%
201377
 
1.0%
51362
 
1.0%
26197
 
0.5%
151169
 
0.4%
109.99164
 
0.4%
Other values (331)2236
 
5.9%
ValueCountFrequency (%)
130694
81.4%
1.31
 
< 0.1%
220
 
0.1%
2.256
 
< 0.1%
2.381
 
< 0.1%
2.431
 
< 0.1%
2.51
 
< 0.1%
2.782
 
< 0.1%
311
 
< 0.1%
3.251
 
< 0.1%
ValueCountFrequency (%)
12000012
 
< 0.1%
5000011
 
< 0.1%
2500012
 
< 0.1%
1250011
 
< 0.1%
1000012
 
< 0.1%
500012
 
< 0.1%
348011
 
< 0.1%
310011
 
< 0.1%
272013
 
< 0.1%
2500111
< 0.1%

sumCauseADonations
Real number (ℝ≥0)

ZEROS

Distinct1182
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2713800983
Minimum0
Maximum5.485917482
Zeros33076
Zeros (%)87.8%
Memory size294.6 KiB
2021-03-23T11:27:46.978085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.404833717
Maximum5.485917482
Range5.485917482
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7738068802
Coefficient of variation (CV)2.851376667
Kurtosis7.035423869
Mean0.2713800983
Median Absolute Deviation (MAD)0
Skewness2.831463512
Sum10227.50177
Variance0.5987770879
MonotocityNot monotonic
2021-03-23T11:27:47.081142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033076
87.8%
1.204119983282
 
0.7%
1.414973348262
 
0.7%
1.707570176173
 
0.5%
1.041392685152
 
0.4%
2.004321374119
 
0.3%
1.322219295114
 
0.3%
1.49136169479
 
0.2%
1.88081359268
 
0.2%
1.55630250163
 
0.2%
Other values (1172)3299
 
8.8%
ValueCountFrequency (%)
033076
87.8%
0.30102999578
 
< 0.1%
0.477121254710
 
< 0.1%
0.60205999133
 
< 0.1%
0.69897000432
 
< 0.1%
0.778151250458
 
0.2%
0.78887511582
 
< 0.1%
0.87506126342
 
< 0.1%
0.95424250944
 
< 0.1%
1.041392685152
 
0.4%
ValueCountFrequency (%)
5.4859174821
 
< 0.1%
5.3221592131
 
< 0.1%
5.1760941542
< 0.1%
5.1092038194
< 0.1%
5.0729957582
< 0.1%
5.0586689181
 
< 0.1%
4.8128117872
< 0.1%
4.7964771961
 
< 0.1%
4.7384760493
< 0.1%
4.7290432352
< 0.1%

sumCauseBDonations
Real number (ℝ≥0)

ZEROS

Distinct277
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.059717547
Minimum0
Maximum4.606392115
Zeros36434
Zeros (%)96.7%
Memory size294.6 KiB
2021-03-23T11:27:47.192850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.606392115
Range4.606392115
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3444077203
Coefficient of variation (CV)5.767278424
Kurtosis41.53761192
Mean0.059717547
Median Absolute Deviation (MAD)0
Skewness6.280315682
Sum2250.575194
Variance0.1186166778
MonotocityNot monotonic
2021-03-23T11:27:47.303140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036434
96.7%
1.414973348118
 
0.3%
1.204119983104
 
0.3%
1.04139268578
 
0.2%
1.70757017663
 
0.2%
0.778151250460
 
0.2%
1.32221929553
 
0.1%
2.00432137436
 
0.1%
1.49136169435
 
0.1%
1.88081359232
 
0.1%
Other values (267)674
 
1.8%
ValueCountFrequency (%)
036434
96.7%
0.096910013011
 
< 0.1%
0.30102999572
 
< 0.1%
0.477121254710
 
< 0.1%
0.602059991312
 
< 0.1%
0.69897000432
 
< 0.1%
0.73158876522
 
< 0.1%
0.778151250460
 
0.2%
0.845098042
 
< 0.1%
0.9030899874
 
< 0.1%
ValueCountFrequency (%)
4.6063921151
< 0.1%
4.243459731
< 0.1%
4.1543156141
< 0.1%
3.676785031
< 0.1%
3.5922878162
< 0.1%
3.5833121521
< 0.1%
3.5793262041
< 0.1%
3.5677319632
< 0.1%
3.5464192672
< 0.1%
3.510545011
< 0.1%

sumCauseCDonations
Real number (ℝ≥0)

ZEROS

Distinct1129
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6726859483
Minimum0
Maximum7.491975679
Zeros25658
Zeros (%)68.1%
Memory size294.6 KiB
2021-03-23T11:27:47.410419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.491361694
95-th percentile2.835371015
Maximum7.491975679
Range7.491975679
Interquartile range (IQR)1.491361694

Descriptive statistics

Standard deviation1.062081245
Coefficient of variation (CV)1.578866405
Kurtosis0.4816764651
Mean0.6726859483
Median Absolute Deviation (MAD)0
Skewness1.278067728
Sum25351.51533
Variance1.128016571
MonotocityNot monotonic
2021-03-23T11:27:47.515237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
025658
68.1%
1.414973348988
 
2.6%
1.707570176898
 
2.4%
2.004321374794
 
2.1%
1.204119983579
 
1.5%
1.322219295363
 
1.0%
1.041392685344
 
0.9%
1.491361694295
 
0.8%
2.303196057272
 
0.7%
1.880813592262
 
0.7%
Other values (1119)7234
 
19.2%
ValueCountFrequency (%)
025658
68.1%
0.30102999575
 
< 0.1%
0.477121254711
 
< 0.1%
0.602059991319
 
0.1%
0.69897000439
 
< 0.1%
0.7781512504135
 
0.4%
0.845098049
 
< 0.1%
0.9030899876
 
< 0.1%
0.95424250949
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
7.4919756792
< 0.1%
6.6627579262
< 0.1%
6.1040091932
< 0.1%
5.7995063081
< 0.1%
5.7709476932
< 0.1%
5.5944122312
< 0.1%
5.4763402711
< 0.1%
5.471119942
< 0.1%
5.4609849932
< 0.1%
5.4372159571
< 0.1%

sumCauseDDonations
Real number (ℝ≥0)

ZEROS

Distinct739
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4200198074
Minimum0
Maximum5.996560265
Zeros29795
Zeros (%)79.1%
Memory size294.6 KiB
2021-03-23T11:27:47.625329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.478566496
Maximum5.996560265
Range5.996560265
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8727693541
Coefficient of variation (CV)2.077924276
Kurtosis2.321035286
Mean0.4200198074
Median Absolute Deviation (MAD)0
Skewness1.888481494
Sum15829.28648
Variance0.7617263455
MonotocityNot monotonic
2021-03-23T11:27:47.730119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029795
79.1%
1.322219295656
 
1.7%
1.707570176446
 
1.2%
2.004321374390
 
1.0%
1.414973348321
 
0.9%
1.041392685303
 
0.8%
1.556302501250
 
0.7%
0.7781512504234
 
0.6%
1.612783857206
 
0.5%
1.491361694167
 
0.4%
Other values (729)4919
 
13.1%
ValueCountFrequency (%)
029795
79.1%
0.0043213737832
 
< 0.1%
0.301029995720
 
0.1%
0.477121254718
 
< 0.1%
0.602059991337
 
0.1%
0.69897000437
 
< 0.1%
0.76342799362
 
< 0.1%
0.7781512504234
 
0.6%
0.81291335661
 
< 0.1%
0.845098043
 
< 0.1%
ValueCountFrequency (%)
5.9965602651
< 0.1%
5.5956913571
< 0.1%
5.3103037381
< 0.1%
5.0382345692
< 0.1%
4.7026114552
< 0.1%
4.6839561221
< 0.1%
4.5859122742
< 0.1%
4.4471735422
< 0.1%
4.4031376871
< 0.1%
4.397957382
< 0.1%

sumCauseEDonations
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct43
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00851169621
Minimum0
Maximum5.787977707
Zeros37580
Zeros (%)99.7%
Memory size294.6 KiB
2021-03-23T11:27:47.836201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5.787977707
Range5.787977707
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1672587505
Coefficient of variation (CV)19.65046055
Kurtosis485.4401375
Mean0.00851169621
Median Absolute Deviation (MAD)0
Skewness21.28377585
Sum320.7802951
Variance0.02797548963
MonotocityNot monotonic
2021-03-23T11:27:47.944076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
037580
99.7%
3.25551371310
 
< 0.1%
2.3996737219
 
< 0.1%
2.0043213747
 
< 0.1%
3.0004340776
 
< 0.1%
1.7075701765
 
< 0.1%
2.3031960575
 
< 0.1%
2.6541765424
 
< 0.1%
1.4149733484
 
< 0.1%
3.1763806924
 
< 0.1%
Other values (33)53
 
0.1%
ValueCountFrequency (%)
037580
99.7%
1.0791812462
 
< 0.1%
1.4149733484
 
< 0.1%
1.7075701765
 
< 0.1%
1.8195439361
 
< 0.1%
2.0043213747
 
< 0.1%
2.1789769472
 
< 0.1%
2.3031960575
 
< 0.1%
2.3996737219
 
< 0.1%
2.5575072022
 
< 0.1%
ValueCountFrequency (%)
5.7879777072
< 0.1%
4.7969280072
< 0.1%
4.6404913752
< 0.1%
4.6299801282
< 0.1%
4.585472012
< 0.1%
4.3217226742
< 0.1%
4.0043643712
< 0.1%
4.0000434271
< 0.1%
3.9777693182
< 0.1%
3.7854010251
< 0.1%

rolling_avg
Real number (ℝ≥0)

SKEWED

Distinct3432
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean703.3424978
Minimum0.01
Maximum1242103.76
Zeros0
Zeros (%)0.0%
Memory size294.6 KiB
2021-03-23T11:27:48.059429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile15
Q125.28333333
median62.5
Q3140
95-th percentile1250
Maximum1242103.76
Range1242103.75
Interquartile range (IQR)114.7166667

Descriptive statistics

Standard deviation9934.6876
Coefficient of variation (CV)14.1249642
Kurtosis6890.998253
Mean703.3424978
Median Absolute Deviation (MAD)37.5
Skewness65.93294395
Sum26506868.71
Variance98698017.7
MonotocityNot monotonic
2021-03-23T11:27:48.182012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1005516
 
14.6%
254692
 
12.4%
504047
 
10.7%
201540
 
4.1%
101206
 
3.2%
30907
 
2.4%
150794
 
2.1%
200777
 
2.1%
15700
 
1.9%
75606
 
1.6%
Other values (3422)16902
44.8%
ValueCountFrequency (%)
0.011
 
< 0.1%
139
0.1%
1.6666666671
 
< 0.1%
219
0.1%
2.0866666671
 
< 0.1%
2.11
 
< 0.1%
2.52
 
< 0.1%
321
0.1%
3.51
 
< 0.1%
3.6666666672
 
< 0.1%
ValueCountFrequency (%)
1242103.761
< 0.1%
458758.33331
< 0.1%
3444001
< 0.1%
3365921
< 0.1%
314394.69331
< 0.1%
302631.5251
< 0.1%
3000001
< 0.1%
297521.48361
< 0.1%
294269.66671
< 0.1%
2678691
< 0.1%

20k_donor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
36864 
1
 
823

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
036864
97.8%
1823
 
2.2%
2021-03-23T11:27:48.376777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T11:27:48.433018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
036864
97.8%
1823
 
2.2%

Most occurring characters

ValueCountFrequency (%)
036864
97.8%
1823
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number37687
100.0%

Most frequent character per category

ValueCountFrequency (%)
036864
97.8%
1823
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common37687
100.0%

Most frequent character per script

ValueCountFrequency (%)
036864
97.8%
1823
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII37687
100.0%

Most frequent character per block

ValueCountFrequency (%)
036864
97.8%
1823
 
2.2%

Interactions

2021-03-23T11:27:00.759871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:00.890627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.002539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.116199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.225396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.332643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.437278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.541201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.638946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.739311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.838626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:01.944163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.042773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.149813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.248387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.343832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.443359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.546909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.652735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.760897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.870259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:02.972122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.079233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.182340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.286632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.391110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.489513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.594684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.702923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.806555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:03.914348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.008774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.112848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.209914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.303451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.402593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.501048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.599709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.699195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.792857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.884545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:04.975067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.068358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.165521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.263209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.355035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.443074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.534454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.622860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.718824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.806500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.897581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:05.984343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.075152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.172897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.270590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.364377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.456380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.554235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.650366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.742024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.833230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:06.928080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.025974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.123577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.218629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.317667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.411069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.511011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.604395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.699772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.789298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.884833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:07.984647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:08.086065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:08.182532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:08.277698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-23T11:27:32.931803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.038877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.142212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.251753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.352082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.455679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.559208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.661023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.761496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.864910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:33.965289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T11:27:34.074284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-23T11:27:40.873340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-23T11:27:48.516940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-23T11:27:48.771428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-23T11:27:49.023026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-23T11:27:49.271402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-23T11:27:49.478548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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A simple visualization of nullity by column.
2021-03-23T11:27:41.992171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcand_idtotalHouseholdDebtprimaryPropertyLoanToValueprimaryPropertyValuepropertyCountisClassADonorisClassBDonorisClassCDonorisClassDDonorNetWorthsumClassADonationsumClassBDonationsumClassCDonationsumClassDDonationmaxClassADonationmaxClassBDonationmaxClassCDonationmaxClassDDonationsumCauseADonationssumCauseBDonationssumCauseCDonationssumCauseDDonationssumCauseEDonationsrolling_avg20k_donor
00candidate_06.0857780.3363802215000.04.00.00.01.01.014011369.01.01.09311.02134.971.01.0751.0751.00.0000000.00.0000000.00.04965.1508331
11candidate_16.4806720.8286643650000.01.00.01.00.01.05812754.01.01751.01.0501.001.01751.01.0251.00.0000000.00.0000000.00.025000.0000001
22candidate_104.6671540.078097595000.01.00.01.00.00.0954112.01.0146.01.01.001.041.01.01.00.0000000.02.1643530.00.085.0000000
33candidate_1005.6919650.2424242029500.03.00.01.01.00.010833760.01.0912.061.51.001.061.016.01.02.9355070.01.7075700.00.0130.8000000
44candidate_10000.0000000.000000394446.01.00.00.00.01.0626163.01.01.01.0126.001.01.01.076.00.0000000.00.0000000.00.035.0000000
55candidate_100005.5861370.498600773366.01.00.01.00.00.0811983.01.014126.01.01.001.014126.01.01.00.0000000.00.0000000.00.0187.5000000
66candidate_1000005.4393330.1610311707751.01.00.00.00.00.03424267.01.01.01.01.001.01.01.01.00.0000000.00.0000000.00.0195.0000000
77candidate_1000010.0000000.0000001049928.01.00.00.00.00.01965455.01.01.01.01.001.01.01.01.00.0000000.00.0000000.00.01000.0000000
88candidate_1000020.0000000.000000887819.01.00.00.00.00.03916996.01.01.01.01.001.01.01.01.00.0000000.00.0000000.00.080.0000000
99candidate_1000035.7275410.311833718333.02.00.00.00.00.01919881.01.01.01.01.001.01.01.01.00.0000000.00.0000000.00.060.0000000

Last rows

df_indexcand_idtotalHouseholdDebtprimaryPropertyLoanToValueprimaryPropertyValuepropertyCountisClassADonorisClassBDonorisClassCDonorisClassDDonorNetWorthsumClassADonationsumClassBDonationsumClassCDonationsumClassDDonationmaxClassADonationmaxClassBDonationmaxClassCDonationmaxClassDDonationsumCauseADonationssumCauseBDonationssumCauseCDonationssumCauseDDonationssumCauseEDonationsrolling_avg20k_donor
3767774593candidate_999883.1892090.003447448517.01.00.00.00.00.0721683.01.01.01.01.01.01.01.01.00.00.00.00.00.0100.00
3767874594candidate_999895.5096000.224877718830.02.00.00.00.00.03118570.01.01.01.01.01.01.01.01.00.00.00.00.00.0100.00
3767974595candidate_999905.5969660.711507555632.02.00.00.00.00.01086061.01.01.01.01.01.01.01.01.00.00.00.00.00.0100.00
3768074596candidate_999914.8427340.0298672331000.01.00.00.00.00.05227531.01.01.01.01.01.01.01.01.00.00.00.00.00.0100.00
3768174597candidate_999925.7993180.945827666049.01.00.00.00.00.0350499.01.01.01.01.01.01.01.01.00.00.00.00.00.0100.00
3768274598candidate_999935.4983110.760862414004.01.00.00.00.00.0301354.01.01.01.01.01.01.01.01.00.00.00.00.00.0200.00
3768374599candidate_999945.1561890.0783071829724.01.00.00.00.00.07756631.01.01.01.01.01.01.01.01.00.00.00.00.00.0150.00
3768474600candidate_999964.1293030.020499657000.01.00.00.00.00.01109093.01.01.01.01.01.01.01.01.00.00.00.00.00.0200.00
3768574601candidate_999975.7150910.1016973031458.02.00.00.00.00.011717466.01.01.01.01.01.01.01.01.00.00.00.00.00.060.00
3768674602candidate_999995.7626790.656262882270.01.00.00.00.00.0878135.01.01.01.01.01.01.01.01.00.00.00.00.00.018.00